Additive manufacturing (AM) process-induced defects make the fatigue life prediction of AM-built parts challenging. A machine learning (ML) framework based on sensitive features and continuous damage mechanics (CDM) herein is proposed to predict the fatigue life of AM-built parts. The sensitive features are extracted to blunt the disturbing effect of causality among the features. The CDM theory considering AM parameters is conducive to constructing a physics-informed ML model. This work employs support vector machines and random forests to predict the fatigue life of AM-built AlSi10Mg alloy. The results demonstrate that the physical knowledge-guided ML model using sensitive features exhibits better performance of fatigue life prediction.

增材制造 (AM) 工艺引起的缺陷使增材制造零件的疲劳寿命预测具有挑战性。本文提出了一种基于敏感特征和连续损伤力学 (CDM) 的机器学习 (ML) 框架来预测增材制造零件的疲劳寿命。提取敏感特征以减弱特征间因果关系的干扰作用。考虑AM参数的CDM理论有利于构建基于物理的ML模型。这项工作采用支持向量机和随机森林来预测 AM 构建的 AlSi10Mg 合金的疲劳寿命。结果表明，使用敏感特征的物理知识引导的机器学习模型表现出更好的疲劳寿命预测性能。